Finding outliers in a dataset is a challenging problem in which traditional analytical methods often perform poorly. As a result, researchers have developed special algorithms for detecting anomalies. In this talk, I will take about three different families of anomaly detection algorithms: Density-based methods, data streaming methods, and time series methods. I will cover both the mathematical and statistical theory behind these algorithms and provide code implementations. Afterwards, I will discuss useful tips I have learned while implementing threat detection systems in practice.